A4 Article in conference proceedings
Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing (2022)
Liu, J., Chang, Z., Min, G., & Han, Z. (2022). Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing. In GLOBECOM 2022 IEEE Global Communications Conference (pp. 3454-3459). IEEE. IEEE Global Communications Conference. https://doi.org/10.1109/GLOBECOM48099.2022.10000933
JYU authors or editors
Publication details
All authors or editors: Liu, Jingyuan; Chang, Zheng; Min, Geyong; Han, Zhu
Parent publication: GLOBECOM 2022 IEEE Global Communications Conference
Place and date of conference: Rio de Janeiro, Brazil, 4.-8.12.2022
ISBN: 978-1-6654-3541-3
eISBN: 978-1-6654-3540-6
Journal or series: IEEE Global Communications Conference
ISSN: 2334-0983
eISSN: 2576-6813
Publication year: 2022
Publication date: 11/01/2023
Pages range: 3454-3459
Publisher: IEEE
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/GLOBECOM48099.2022.10000933
Publication open access: Not open
Publication channel open access:
Abstract
Federated learning (FL) is a type of distributed machine learning in which mobile users can train data locally and send the results to the FL server to update the global model. However, the implementation of FL may be prevented by the self-fish nature of mobile users, as they need to contribute considerable data and computing resources for participating in the FL process. Therefore, it is of importance to design the incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission, we design an incentive scheme to facilitate the FL process by investigating interactions between the multi-access edge computing (MEC) server and mobile users in a MEC-based FL system. By using a two-stage Stackelberg game model, we explore the transmission power allocation of the users and reward policy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results show that our model is effective for different parameter settings and the utility of the MEC server can be increased significantly compared to the baseline.
Keywords: machine learning; edge computing; mobile devices; wireless data transmission; resource allocation; game theory
Free keywords: Federated learning; multi-access edge computing; incentive mechanism; power allocation
Contributing organizations
Ministry reporting: Yes
VIRTA submission year: 2022
JUFO rating: 1
